code: def get_filter_config(shape):
      """Returns a default Conv2D filter config (list) for a given image shape.
  
      Args:
          shape (Tuple[int]): The input (image) shape, e.g. (84,84,3).
  
      Returns:
          List[list]: The Conv2D filter configuration usable as `conv_filters`
              inside a model config dict.
      """
      # 96x96x3 (e.g. CarRacing-v0).
      filters_96x96 = [
          [16, [8, 8], 4],
          [32, [4, 4], 2],
          [256, [11, 11], 2],
      ]
      # Atari.
      filters_84x84 = [
          [16, [8, 8], 4],
          [32, [4, 4], 2],
          [256, [11, 11], 1],
      ]
      # Dreamer-style (S-sized model) Atari or DM Control Suite.
      filters_64x64 = [
          [32, [4, 4], 2],
          [64, [4, 4], 2],
          [128, [4, 4], 2],
          [256, [4, 4], 2],
      ]
      # Small (1/2) Atari.
      filters_42x42 = [
          [16, [4, 4], 2],
          [32, [4, 4], 2],
          [256, [11, 11], 1],
      ]
      # Test image (10x10).
      filters_10x10 = [
          [16, [5, 5], 2],
          [32, [5, 5], 2],
      ]
  
      shape = list(shape)
      if len(shape) in [2, 3] and (
          shape[:2] == [96, 96] or shape[1:] == [96, 96]
      ):
          return filters_96x96
      elif len(shape) in [2, 3] and (
          shape[:2] == [84, 84] or shape[1:] == [84, 84]
      ):
          return filters_84x84
      elif len(shape) in [2, 3] and (
          shape[:2] == [64, 64] or shape[1:] == [64, 64]
      ):
          return filters_64x64
      elif len(shape) in [2, 3] and (
          shape[:2] == [42, 42] or shape[1:] == [42, 42]
      ):
          return filters_42x42
      elif len(shape) in [2, 3] and (
          shape[:2] == [10, 10] or shape[1:] == [10, 10]
      ):
          return filters_10x10
      else:
          raise ValueError(
              "No default configuration for obs shape {}".format(shape)
              + ", you must specify `conv_filters` manually as a model option. "
              "Default configurations are only available for inputs of the following "
              "shapes: [42, 42, K], [84, 84, K], [64, 64, K], [10, 10, K]. You may "
              "alternatively want to use a custom model or preprocessor."
          )
  
file_path: ray/python/ray/rllib/models/utils.py
imports: from typing import Callable, Optional, Union
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.framework import try_import_jax, try_import_tf, try_import_torch
label_desc: Use image node_id 4 for calling a global function with example usage: get_filter_config(shape) and returns: filters_96x96, filters_84x84, filters_64x64, filters_42x42, filters_10x10